Subject-Aware Explainable Contrastive Deep Fusion Learning for Anxiety Level Analysis

Abstract

We propose a contrastive learning deep fusion neural network for effectively classifying subjects’ anxiety levels. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by ensuring subject representation within the training phase to boost self-supervised cross-subject feature learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying anxiety levels by achieving a classification accuracy of 97.67% while also identifying influential brain regions.